logistic回归python实现代码

当然可以回答您的问题。以下是一个简单的 logistic 回归在 Python 中的实现代码：

import numpy as np

class LogisticRegression:
    def __init__(self, learning_rate=0.01, num_iterations=10000, fit_intercept=True):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        self.fit_intercept = fit_intercept

    def add_intercept(self, X_data):
        intercept = np.ones((X_data.shape[0], 1))
        return np.concatenate((intercept, X_data), axis=1)

    def sigmoid_function(self, z):
        return 1 / (1 + np.exp(-z))

    def gradient_descent(self, X_data, y_data, theta):
        m = len(y_data)
        for i in range(self.num_iterations):
            z = np.dot(X_data, theta)
            h = self.sigmoid_function(z)
            gradient = np.dot(X_data.T, (h - y_data)) / m
            theta -= self.learning_rate * gradient

        return theta

    def fit(self, X_data, y_data):
        if self.fit_intercept:
            X_data = self.add_intercept(X_data)

        self.theta = np.zeros(X_data.shape[1])
        self.theta = self.gradient_descent(X_data, y_data, self.theta)

    def predict_prob(self, X_data):
        if self.fit_intercept:
            X_data = self.add_intercept(X_data)

        return self.sigmoid_function(np.dot(X_data, self.theta))

    def predict(self, X_data, threshold=0.5):
        return self.predict_prob(X_data) >= threshold

这里实现了一个 LogisticRegression 类，包含了常见的功能如梯度下降、sigmoid 函数等。可以通过调用类中的方法来进行训练和预测。